Dr. Ranadip Pal Receives Prestigious NSF CAREER Award for Genetic Regulatory Network Modeling and Control


Edited by Javad Hashemi and Jeff Sammons

Dr. Ranadip Pal
Dr. Ranadip Pal, Assistant Professor in the Department of Electrical and Computer Engineering

Dr. Ranadip Pal, an assistant professor of electrical and computer engineering at Texas Tech, has been selected to receive a 2010 NSF CAREER Award from the National Science Foundation. Pal's award recognizes his career development plan entitled, "Robustness in Genetic Regulatory Network Modeling and Control." His research focuses on genomic signal processing and control of genetic regulatory networks.

When a person is diagnosed with cancer, the treatment is often in the form of “one size fits all” approach. Subjects receive a class of drugs and dosages that work to destroy the malignant cells. Pal is researching a theory with a personalized approach. The theory addresses cancer with a consideration of a patient's individual genetic makeup, or their genetic regulatory network. In this way, a personalized “attack plan” can be formulated with specific drugs and dosages. Therefore, patients can receive the best personalized treatment based on the interaction between cancer-fighting drugs and the patient’s unique genetic qualities. A new generation of cancer drugs has been developed in the last decade with a highly-specific impact on cancer cells. The success of these targeted drugs has been limited in the past because the drug selections and dosages have been based on empirical principles, and not mathematical models.

“These drugs target only the cancerous cells and they do not damage the healthy cells,” Pal said. “In chemotherapy, there are numerous side effects, including hair loss and general nausea, that are related to destruction of healthy cells. We want to understand each patient’s genetic makeup so that we can administer the drugs that will work the best for them and their form of cancer.”

Genomic Signal Processing
Genomic signal processing within a genetic regulatory network.

Pal is developing a theoretical and computational framework to estimate the uncertainties in genetic regulatory network modeling so that robust therapeutic strategies for genetic diseases, such as cancer, can be developed.

“Once we develop robust therapeutic models and strategies, we can develop a subject-specific policy for what drugs should be used, how much should be used, and how frequently the treatment should take place,” Pal said.

One of the objectives of genetic regulatory network modeling is to design and analyze therapeutic intervention strategies aimed at moving the network out of undesirable states — such as those associated with disease — and into desirable ones. However, limited experimental data prevent accurate estimation of the mathematical model of the gene regulatory network. To ensure the success of a mathematically-designed model and intervention strategy for genetic diseases, it is critical to know how errors in modeling could have an effect on the outcome of the treatment. To increase the effectiveness of cancer therapies in the clinical setting are increased, intervention strategies with robustness will be necessary. Pal’s research aims to shed light on the limitations of the performance of mathematically-designed intervention strategies, while ensuring that the designed control strategy produces the optimal outcome in presence of particular modeling uncertainties.

Pal’s research is being carried out on existing human cancer cell lines and targeted therapy in mice models, in collaboration with the Translational Genomics Research Institute and University of Texas Health Sciences Center at San Antonio.

The potential impact of this research on cancer treatments may be far reaching. According to Pal, “Even if we can apply a basic drug treatment strategy based on the average genetic regulatory model for a specific type of cancer, the success rate of cancer treatments can improve.”

The nature of this research promotes collaboration between the disciplines of electrical engineering and systems biology through research and education. Pal is developing an interdisciplinary graduate course on genetic regulatory network modeling and control, and an undergraduate course on engineering applications in biology. He is also planning to establish a research laboratory at Texas Tech that prepares graduate students with skills in genomic signal processing (GSP) research, as well as involving undergraduates in GSP research. The activities with undergraduate students will be in collaboration with the Texas Tech Howard Hughes Medical Institute Undergraduate Science Education Program.

Pal joined the Department of Electrical and Computer Engineering in the Whitacre College of Engineering as an assistant professor in 2007. He received a Master of Science and a Doctor of Philosophy in Electrical Engineering from Texas A&M University in 2004 and 2007, respectively, and a Bachelor of Technology in Electronics and Electrical Communication Engineering from IIT Kharagpur in 2002.

He has authored more than 15 journal publications and was the recipient of the Ebsenberger/Fouraker Fellowship, a Distinguished Graduate Student Masters Research Award, and a National Instruments Fellowship while at Texas A&M. He was also an Indian National Math Olympiad Awardee and was ranked first in the Regional Math Olympiad, West Bengal, India.